Canine Vaginal Leiomyoma Diagnosed by CT Vaginourethrography
Bibliographic record
Abstract
A 13 yr old female spayed Labrador retriever presented for vulvar bleeding. Abdominal radiographs revealed a soft tissue mass in the ventral pelvic canal. A computed tomography (CT) exam and a CT vaginourethrogram localized the mass to the vagina, helped further characterize the mass, and aided in surgical planning. A total vaginectomy was performed and the histologic diagnosis was leiomyoma. Vaginal tumors make up 1.9-3% of all tumors. Seventy-three percent of vaginal tumors are benign, and 83% of those are leiomyomas. Leiomyomas often have a good long-term prognosis with surgical resection. The diagnostic investigation of this case report utilized a multimodal imaging approach to determine the extent and respectability of the vaginal mass. To the best of the authors' knowledge, this is the first report describing a CT vaginourethrogram.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".